[BioC] Non linear intensities

Kasper Daniel Hansen khansen at stat.berkeley.edu
Fri Jul 24 01:29:08 CEST 2009

This is well known and is (one of) the reason(s) for the difficulty  
with analyzing microarrays.  Note that if you compute fold change, it  
looks a lot better which is why we use fold change.  Also note that  
normalization (hopefully) will help you a bit.

But in the end there will be genes where the fold change is not great  
either (like 'C" in your example), and you will need to learn how to  
live with it.

There are no great way to correct for it, since the correction  
typically depends on the probe used to measure the signal.  If you had  
the money and ability to generate a sample in which _all_ genes are  
expressed and you then do a dilution experiment you might be able to  
estimate this.  There are attempts at correcting for this  
computationally, but they are typically not very impressive.

For more details, see the 100s of papers on microarray analysis and  
probe effects.


On Jul 23, 2009, at 8:25 , David martin wrote:

> Hello,
> I have a naive question. In my experiment i have computed different  
> standard curves for different genes.
> I have noticed that my data is not following a linear pattern (sorry  
> i'm not statistician so don't know if this is the word).
> For e.g
> GENES	[1µg]	[2µg] [5µg] [10µg]
> gene A	500	800	1800	3500
> gene B	450	650	1700	3400
> gene C	200	300	600	1300
> As you can see the intensities are not as I would expect (there is  
> no linear intensity based on the concentration); Since my data shows  
> that there is a bias i would like to correct the data and adjust  
> according.
> For instance, i think there might be a coeffecient, which based on  
> the intensitiy would help to correct the intenstities. At the end if  
> for a gene X i find an intensity of 500 i could assume that it  
> should higher than that to have a 2 fold change and that it should  
> probably be somewhere around 1000.
> Thanks for your help ??
> thanks for your help.
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